ABSTRACT
A synthetic population is a simplified microscopic representation of an actual population. Statistically representative at the population level, it provides valuable inputs to simulation models (especially agent-based models) in research areas such as transportation, land use, economics, and epidemiology. This article describes the datasets from the Synthetic Sweden Mobility (SySMo) model using the state-of-art methodology, including machine learning (ML), iterative proportional fitting (IPF), and probabilistic sampling. The model provides a synthetic replica of over 10 million Swedish individuals (i.e., agents), their household characteristics, and activity-travel plans. This paper briefly explains the methodology for the three datasets: Person, Households, and Activity-travel patterns. Each agent contains socio-demographic attributes, such as age, gender, civil status, residential zone, personal income, car ownership, employment, etc. Each agent also has a household and corresponding attributes such as household size, number of children ≤ 6 years old, etc. These characteristics are the basis for the agents' daily activity-travel schedule, including type of activity, start-end time, duration, sequence, the location of each activity, and the travel mode between activities.
ABSTRACT
To address the sustainability challenges related to travel behavior, technological innovations will not be enough. Behavioral changes are also called for. The aim of the present study is to examine the influence of sociodemography, geography, and personality on car driving and use of public transportation. Sociodemographic factors have been defined by age, gender, income, and education. Geographic factors have been studied through residential area (e.g., rural and urban areas). Personality has been studied through the Five-Factor-Model of personality-degree of Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism. The analysis is based on a survey with 1812 respondents, representative for the Swedish population. Regarding sociodemographic factors, car driving is explained by being male, higher age, higher income, while use of public transportation is explained by lower age and higher education. The user profile of a car driver is the opposite to that of a public transport passenger when it comes to geographic factors; urban residential area explains public transportation while rural area explains car driving. Some personality factors are also opposites; a low degree of Openness and a high degree of Extraversion explain car driving, while a high degree of Openness and a low degree of Extraversion explain use of public transportation. Moreover, car driving is explained by a low degree of Neuroticism, while use of public transportation is explained by a low degree of Conscientiousness and a high degree of Agreeableness. Since sociodemography, geography, and personality influence how people process information and evaluate market propositions (e.g., products and services), the findings presented here are useful for policymakers and transportations planners who would like to change behavior from car driving to public transportation use. Caution should be taken in interpreting the relationship between personality traits and transportation modes, since the personality traits are measured by a short scale (i.e., Big Five Inventory (BFI)-10), with limitations in the factor structure for a representative sample of the Swedish population.
